Transformer

NVIDIA-MambaVision 摘要 主要工作：integrating Vision Transformers (ViT) with Mamba， 目的：improves its capacity to capture long-range spatial dependencies 适用于哪些下游任务：object detection, instance segmentation,and semantic segmentation 开源链接：GitHub - NVlabs/MambaVision: [CVPR 2025] Official PyTorch Implementation of MambaVision: A Hybrid Mamba-Transformer Vision Backbone 引言 transformer训练成本高：the quadratic complexity of the attention mechanism with respect to sequence length makes Transformers computationally expensive to train and deploy 本篇的前置知识：Vit、Mamba、SSM 等 Mamba 通过 new State Space Model (SSM) 关注该关注的，通过ardware-aware considerations并行计算：new State Space Model (SSM) that achieves linear time complexity，enables efficient input-dependent processing of long sequences with ardware-aware considerations. ...

1. 理论 输入 embedding words turning each input word into a vector using an embedding algorithm. 问题：The size of this list is hyperparameter we can set – basically it would be the length of the longest sentence in our training dataset. 最底层的编码器输入是 embedding words，其后都是其他编码器的输出 In the bottom encoder that would be the word embeddings, but in other encoders, it would be the output of the encoder that’s directly below BERT实践中也提到了这个，可以查看下 ...